Zobrazeno 1 - 10
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pro vyhledávání: '"Josephs, Nathaniel"'
Autor:
Josephs, Nathaniel, Upton, Elizabeth
In team sports, traditional ranking statistics do not allow for the simultaneous evaluation of both individuals and combinations of players. Metrics for individual player rankings often fail to consider the interaction effects between groups of playe
Externí odkaz:
http://arxiv.org/abs/2403.20214
We introduce the nested stochastic block model (NSBM) to cluster a collection of networks while simultaneously detecting communities within each network. NSBM has several appealing features including the ability to work on unlabeled networks with pot
Externí odkaz:
http://arxiv.org/abs/2307.09210
Most businesses impose a supervisory hierarchy on employees to facilitate management, decision-making, and collaboration, yet routine inter-employee communication patterns within workplaces tend to emerge more naturally as a consequence of both super
Externí odkaz:
http://arxiv.org/abs/2208.01208
The increasing prevalence of network data in a vast variety of fields and the need to extract useful information out of them have spurred fast developments in related models and algorithms. Among the various learning tasks with network data, communit
Externí odkaz:
http://arxiv.org/abs/2203.02090
Autor:
Josephs, Nathaniel
Networks are quickly becoming one of the most common data types across diverse disciplines from the biological to the social sciences. Consequently, the study of networks as data objects is fundamental to developing statistical methodology for answer
Externí odkaz:
https://hdl.handle.net/2144/43216
The network scale-up method (NSUM) is a survey-based method for estimating the number of individuals in a hidden or hard-to-reach subgroup of a general population. In NSUM surveys, sampled individuals report how many others they know in the subpopula
Externí odkaz:
http://arxiv.org/abs/2111.09684
There is a growing literature on the statistical analysis of multiple networks in which the network is the fundamental data object. However, most of this work requires networks on a shared set of labeled vertices. In this work, we consider the questi
Externí odkaz:
http://arxiv.org/abs/2104.14952
In this paper, we propose a new spectral-based approach to hypothesis testing for populations of networks. The primary goal is to develop a test to determine whether two given samples of networks come from the same random model or distribution. Our t
Externí odkaz:
http://arxiv.org/abs/2011.12416
While the study of a single network is well-established, technological advances now allow for the collection of multiple networks with relative ease. Increasingly, anywhere from several to thousands of networks can be created from brain imaging, gene
Externí odkaz:
http://arxiv.org/abs/2004.04765
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